Literature DB >> 19762922

Bidirectional texture function modeling: a state of the art survey.

Jirí Filip1, Michal Haindl.   

Abstract

An ever-growing number of real-world computer vision applications require classification, segmentation, retrieval, or realistic rendering of genuine materials. However, the appearance of real materials dramatically changes with illumination and viewing variations. Thus, the only reliable representation of material visual properties requires capturing of its reflectance in as wide range of light and camera position combinations as possible. This is a principle of the recent most advanced texture representation, the Bidirectional Texture Function (BTF). Multispectral BTF is a seven-dimensional function that depends on view and illumination directions as well as on planar texture coordinates. BTF is typically obtained by measurement of thousands of images covering many combinations of illumination and viewing angles. However, the large size of such measurements has prohibited their practical exploitation in any sensible application until recently. During the last few years, the first BTF measurement, compression, modeling, and rendering methods have emerged. In this paper, we categorize, critically survey, and psychophysically compare such approaches, which were published in this newly arising and important computer vision and graphics area.

Mesh:

Year:  2009        PMID: 19762922     DOI: 10.1109/TPAMI.2008.246

Source DB:  PubMed          Journal:  IEEE Trans Pattern Anal Mach Intell        ISSN: 0098-5589            Impact factor:   6.226


  4 in total

1.  Lightdrum-Portable Light Stage for Accurate BTF Measurement on Site.

Authors:  Vlastimil Havran; Jan Hošek; Šárka Němcová; Jiří Čáp; Jiří Bittner
Journal:  Sensors (Basel)       Date:  2017-02-23       Impact factor: 3.576

Review 2.  Design and implementation of practical bidirectional texture function measurement devices focusing on the developments at the University of Bonn.

Authors:  Christopher Schwartz; Ralf Sarlette; Michael Weinmann; Martin Rump; Reinhard Klein
Journal:  Sensors (Basel)       Date:  2014-04-28       Impact factor: 3.576

3.  Rapid material appearance acquisition using consumer hardware.

Authors:  Jiří Filip; Radomír Vávra; Mikuláš Krupička
Journal:  Sensors (Basel)       Date:  2014-10-22       Impact factor: 3.576

4.  3D surface texture analysis of high-resolution normal fields for facial skin condition assessment.

Authors:  Alassane Seck; Hannah Dee; William Smith; Bernard Tiddeman
Journal:  Skin Res Technol       Date:  2019-09-28       Impact factor: 2.365

  4 in total

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